Step 1: Define design parameters | Step 2: Run sim_fixed_n() | Step 3: Summarize simulations
Package: simtrial 1.0.2
Yujie Zhao
simtrial: Clinical Trial Simulation
Provides some basic routines for simulating a clinical trial. The primary intent is to provide some tools to generate trial simulations for trials with time to event outcomes. Piecewise exponential failure rates and piecewise constant enrollment rates are the underlying mechanism used to simulate a broad range of scenarios such as those presented in Lin et al. (2020) <doi:10.1080/19466315.2019.1697738>. However, the basic generation of data is done using pipes to allow maximum flexibility for users to meet different needs.
Authors:
simtrial_1.0.2.tar.gz
simtrial_1.0.2.zip(r-4.7)simtrial_1.0.2.zip(r-4.6)simtrial_1.0.2.zip(r-4.5)
simtrial_1.0.2.tgz(r-4.6-x86_64)simtrial_1.0.2.tgz(r-4.6-arm64)simtrial_1.0.2.tgz(r-4.5-x86_64)simtrial_1.0.2.tgz(r-4.5-arm64)
simtrial_1.0.2.tar.gz(r-4.7-arm64)simtrial_1.0.2.tar.gz(r-4.7-x86_64)simtrial_1.0.2.tar.gz(r-4.6-arm64)simtrial_1.0.2.tar.gz(r-4.6-x86_64)
simtrial_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
simtrial/json (API)
| # Install 'simtrial' in R: |
| install.packages('simtrial', repos = c('https://merck.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/merck/simtrial/issues
Pkgdown/docs site:https://merck.github.io
Datasets:
- ex1_delayed_effect - Time-to-event data example 1 for non-proportional hazards working group
- ex2_delayed_effect - Time-to-event data example 2 for non-proportional hazards working group
- ex3_cure_with_ph - Time-to-event data example 3 for non-proportional hazards working group
- ex4_belly - Time-to-event data example 4 for non-proportional hazards working group
- ex5_widening - Time-to-event data example 5 for non-proportional hazards working group
- ex6_crossing - Time-to-event data example 6 for non-proportional hazards working group
- mb_delayed_effect - Simulated survival dataset with delayed treatment effect
Last updated from:687749ba5c. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 298 | ||
| linux-devel-x86_64 | OK | 310 | ||
| source / vignettes | OK | 296 | ||
| linux-release-arm64 | OK | 265 | ||
| linux-release-x86_64 | OK | 300 | ||
| macos-release-arm64 | OK | 182 | ||
| macos-release-x86_64 | OK | 593 | ||
| macos-oldrel-arm64 | OK | 152 | ||
| macos-oldrel-x86_64 | OK | 336 | ||
| windows-devel | OK | 292 | ||
| windows-release | OK | 265 | ||
| windows-oldrel | OK | 300 | ||
| wasm-release | OK | 185 |
Exports:as_gtcounting_processcreate_cutcreate_testcut_data_by_datecut_data_by_eventearly_zerofhfit_pwexpget_analysis_dateget_cut_date_by_eventmaxcombombmilestonemultitestrandomize_by_fixed_blockrmstrpwexprpwexp_enrollsim_fixed_nsim_gs_nsim_pw_survto_sim_pw_survwlr
Dependencies:codetoolsdata.tabledigestdoFutureforeachfuturefuture.applyglobalsiteratorslatticelistenvMatrixmvtnormparallellyRcppsurvival
Simulate Fixed Designs with Ease via sim_fixed_n
Last update: 2025-06-11
Started: 2025-03-21
Simulate Group Sequential Designs with Ease via sim_gs_n
Step 1: Define design paramaters | Step 2: Run sim_gs_n() | Step 3: Summarize simulations | References
Last update: 2025-06-11
Started: 2025-03-21
Simulating time-to-event trials in parallel
Overview | Background | The sequential run | Setting up a parallel backend | Execution in parallel | A nested parallel example
Last update: 2025-04-03
Started: 2023-09-25
Custom Fixed Design Simulations: A Tutorial on Writing Code from the Ground Up
Step 1: Simulate time-to-event data | Scenario a) The simplest scenario | Scenario b) Differential dropout rates | Scenario c) Stratified designs | Scenario d) Multi-arm designs | Step 2: Cut data | Step 3: Run tests | Step 4: Perform the above single simulation repeatedly | Step 5: Summarize simulations | References
Last update: 2025-03-25
Started: 2025-03-21
Note on potential discrepancies between simtrial and survdiff
Overview | Scenario definitions | A scenario that generates a discrepancy
Last update: 2025-03-13
Started: 2025-01-31
TTE simulation data manipulations
Overview | Results data table | Simulated trial dataset generation | Dataset manipulations for analysis | Flow for simulating group sequential: one scenario algorithm
Last update: 2024-08-08
Started: 2023-05-30
Using the Magirr-Burman weights for testing
Introduction | Simulating a delayed effect example | Generalizing the Magirr-Burman test | Freidlin and Korn strong null hypothesis example | References
Last update: 2024-08-08
Started: 2023-11-28
Restricted mean survival time (RMST)
Introduction | RMST vs. logrank | Estimation of RMST in a single arm at a single time point | $$\hat{S}(t_i) | \prod_{j=1}^{i}\left(1-\frac{d_{j}}{Y_{j}}\right)$$Based on the definition and formula above, $\text{RMST}(\tau)$ can be estimated by$$\widehat{\text{RMST}}(\tau) | \int_{0}^{\tau} \hat{S}(t) d t | The standard error of $\widehat | \widehat{\text{Var}}(\widehat{\text{RMST}}(\tau)) | \sqrt | \int_{t_i}^{\tau} \hat{S}(t) d t | Estimation of RMST differences in 2 arms at a single time point | References
Last update: 2024-07-23
Started: 2024-02-05
Approximating an arbitrary hazard function
Last update: 2024-05-07
Started: 2023-11-28
Basic tools for time-to-event trial simulation and testing
Overview | Randomization | Enrollment | Time-to-event and time-to-dropout | Generating a trial | Cutting data for analysis | Generating a counting process dataset | Logrank and weighted logrank testing | Simplification for 2-arm trials
Last update: 2024-05-07
Started: 2023-11-28
Computing p-values for Fleming-Harrington weighted logrank tests and the MaxCombo test
Introduction | Defining the test | Executing for a single dataset | Generating test statistics with sim_fixed_n() | Generating data with sim_pw_surv() | Using survival data in another format | Simulation | References
Last update: 2024-05-07
Started: 2024-04-11
